Preweights, stored as part of each respondent’s data or created during the edit, are applied to individual records before target or factor weighting is applied. When the characteristic weights are targets, the preweights are used in the calculation of the weight for each respondent. For example, suppose that each of the 380 housewives has a preweight in columns 181 to 189 of their data record: one has the value 10 in c(181,189), while for another the weight in that field is 20. If all the rest have a weight of 1, you have:

female shoppers instead of the original 380.

To reach the target of 10,000, the weight for each woman would be:

Without preweights, all these women would receive a weight of 26.32.

Preweights are often used in studies which deal with newspaper readership, or the like, where a male adult respondent in a household is counted as the total number of male adults in the household, on the theory that the other males will probably have the same demographics and similar behavioral patterns. Another use is in political polls, where a respondent is preweighted by the number of calls it took to reach them. The supposition behind this theory is that the more calls it takes to reach a respondent, the more people there are like them, who are equally hard to reach. The respondent must therefore be preweighted in order to help represent the many like them who were never interviewed.